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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

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A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry.

Vasileios Papapanagiotou¹, Christos Diou¹, Lingchuan Zhou²

¹Multimedia Understanding Group, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.

IEEE Journal of Biomedical and Health Informatics

|November 12, 2016

Summary

This summary is machine-generated.

This study introduces a novel wearable system using an in-ear microphone and photoplethysmography (PPG) sensor for accurate chewing detection. Fusing audio and PPG signals significantly enhances eating event detection accuracy.

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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

Area of Science:

Biomedical Engineering
Human-Computer Interaction
Wearable Technology

Background:

Accurate dietary management requires precise monitoring of eating habits.
Wearable sensors and smartphones offer objective, real-time behavioral measurements.
Existing chewing detection methods often rely on single sensor types, limiting accuracy.

Purpose of the Study:

To develop a high-accuracy, low-sampling-rate prototype system for chewing detection.
To combine in-ear microphone and photoplethysmography (PPG) sensor data for improved eating detection.
To evaluate the system's effectiveness in a semi-free living environment.

Main Methods:

A pipeline processing individual sensor signals before fusion was proposed.
Feature extraction was performed on audio and PPG data separately.
Support vector machine (SVM) classifiers were used for detection, with a late-fusion scheme combining classifier scores.

Main Results:

The combined audio and PPG signals significantly improved eating event detection effectiveness.
The system achieved a maximum accuracy of 0.938.
Class-weighted accuracy reached up to 0.892.

Conclusions:

Fusing audio and PPG sensor data offers a significant improvement for eating detection systems.
The developed prototype demonstrates high accuracy and potential for dietary management applications.
This approach provides a robust method for objective, real-time monitoring of eating behavior.